{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/sebastiantonke/Desktop/Replication Package/Replication files experiment/output_logs/table 5.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}20 Jul 2023, 08:50:50
{txt}
{com}. 
. 
. gen ebalancematch=0 if itt==.
{txt}(207,212 missing values generated)

{com}. replace ebalancematch=1 if itt==0
{txt}(69,124 real changes made)

{com}. 
. **** Ebalance matching ****
. ebalance ebalancematch meanasinhinvoice inactive prenoconsumption paidcount1 paidcount2 meanaveragepayment1 meanaveragepayment2 preclosingbalancew preage_of_accountw presumpaymentratiow if t==12, target(2)
{res}

Data Setup
{txt}Treatment variable:   {res}ebalancematch
{txt}Covariate adjustment:{res} meanasinhinvoice inactive prenoconsumption paidcount1 paidcount2 meanaveragepayment1 meanaveragepayment2 preclosingbalancew preage_of_accountw presumpaymentratiow {txt}(1st order).{res} meanasinhinvoice inactive prenoconsumption paidcount1 paidcount2 meanaveragepayment1 meanaveragepayment2 preclosingbalancew preage_of_accountw presumpaymentratiow {txt}(2nd order).

{res}Optimizing...
{txt}Iteration 1: Max Difference = {res}57566.1658{txt}
{txt}Iteration 2: Max Difference = {res}21176.2374{txt}
{txt}Iteration 3: Max Difference = {res}7789.13116{txt}
{txt}Iteration 4: Max Difference = {res}2864.29079{txt}
{txt}Iteration 5: Max Difference = {res}1052.54567{txt}
{txt}Iteration 6: Max Difference = {res}386.047564{txt}
{txt}Iteration 7: Max Difference = {res}140.872372{txt}
{txt}Iteration 8: Max Difference = {res}50.7186708{txt}
{txt}Iteration 9: Max Difference = {res}17.6535747{txt}
{txt}Iteration 10: Max Difference = {res}5.69767447{txt}
{txt}Iteration 11: Max Difference = {res}1.59199904{txt}
{txt}Iteration 12: Max Difference = {res}.326369447{txt}
{txt}Iteration 13: Max Difference = {res}.03479158{txt}
{txt}Iteration 14: Max Difference = {res}.000719446{txt}
{txt}maximum difference smaller than the tolerance level; {res}convergence achieved


Treated units: {txt}3046{col 24}{res}total of weights: {txt}3046
{res}Control units: {txt}7090{col 24}{res}total of weights: {txt}3046


{res}Before: {txt}without weighting
{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treat}{space 1}{c |}{space 1}{rcenter 31:Control}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:meanasinhi~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    4.375}}}{space 1}{space 1}{ralign 9:{res:{sf:    1.352}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.6963}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    4.166}}}{space 1}{space 1}{ralign 9:{res:{sf:     2.38}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.7568}}}{space 1}
{space 0}{space 0}{ralign 12:inactive}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .1074}}}{space 1}{space 1}{ralign 9:{res:{sf:   .09586}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.537}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .2312}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1778}}}{space 1}{space 1}{ralign 9:{res:{sf:    1.275}}}{space 1}
{space 0}{space 0}{ralign 12:prenoconsu~n}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .1114}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03335}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.218}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     .173}}}{space 1}{space 1}{ralign 9:{res:{sf:   .07133}}}{space 1}{space 1}{ralign 9:{res:{sf:    1.681}}}{space 1}
{space 0}{space 0}{ralign 12:paidcount1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3465}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06671}}}{space 1}{space 1}{ralign 9:{res:{sf:    .5493}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3285}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06789}}}{space 1}{space 1}{ralign 9:{res:{sf:    .6024}}}{space 1}
{space 0}{space 0}{ralign 12:paidcount2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3659}}}{space 1}{space 1}{ralign 9:{res:{sf:    .0678}}}{space 1}{space 1}{ralign 9:{res:{sf:    .4617}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3254}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06893}}}{space 1}{space 1}{ralign 9:{res:{sf:    .5576}}}{space 1}
{space 0}{space 0}{ralign 12:meanaverag~1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    4.391}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.757}}}{space 1}{space 1}{ralign 9:{res:{sf:   -1.076}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    4.301}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.553}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.9203}}}{space 1}
{space 0}{space 0}{ralign 12:meanavera~t2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    4.456}}}{space 1}{space 1}{ralign 9:{res:{sf:      4.2}}}{space 1}{space 1}{ralign 9:{res:{sf:   -1.257}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    4.199}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.571}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.9173}}}{space 1}
{space 0}{space 0}{ralign 12:preclosing~w}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    507.9}}}{space 1}{space 1}{ralign 9:{res:{sf:  1620571}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.018}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    684.3}}}{space 1}{space 1}{ralign 9:{res:{sf:  2746101}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.852}}}{space 1}
{space 0}{space 0}{ralign 12:preage_of_~w}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    44.34}}}{space 1}{space 1}{ralign 9:{res:{sf:     1533}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.089}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     81.2}}}{space 1}{space 1}{ralign 9:{res:{sf:     3775}}}{space 1}{space 1}{ralign 9:{res:{sf:     .536}}}{space 1}
{space 0}{space 0}{ralign 12:presumpaym~w}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .9038}}}{space 1}{space 1}{ralign 9:{res:{sf:    .3269}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.906}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .9289}}}{space 1}{space 1}{ralign 9:{res:{sf:    .5123}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.695}}}{space 1}


{res}After:  {txt}_webal as the weighting variable
{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treat}{space 1}{c |}{space 1}{rcenter 31:Control}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:meanasinhi~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    4.375}}}{space 1}{space 1}{ralign 9:{res:{sf:    1.352}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.6963}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    4.375}}}{space 1}{space 1}{ralign 9:{res:{sf:    1.352}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.5861}}}{space 1}
{space 0}{space 0}{ralign 12:inactive}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .1074}}}{space 1}{space 1}{ralign 9:{res:{sf:   .09586}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.537}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .1074}}}{space 1}{space 1}{ralign 9:{res:{sf:   .09586}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.536}}}{space 1}
{space 0}{space 0}{ralign 12:prenoconsu~n}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .1114}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03335}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.218}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .1114}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03335}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.327}}}{space 1}
{space 0}{space 0}{ralign 12:paidcount1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3465}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06671}}}{space 1}{space 1}{ralign 9:{res:{sf:    .5493}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3465}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06671}}}{space 1}{space 1}{ralign 9:{res:{sf:    .5773}}}{space 1}
{space 0}{space 0}{ralign 12:paidcount2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3659}}}{space 1}{space 1}{ralign 9:{res:{sf:    .0678}}}{space 1}{space 1}{ralign 9:{res:{sf:    .4617}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3659}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06781}}}{space 1}{space 1}{ralign 9:{res:{sf:    .4509}}}{space 1}
{space 0}{space 0}{ralign 12:meanaverag~1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    4.391}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.757}}}{space 1}{space 1}{ralign 9:{res:{sf:   -1.076}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    4.391}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.757}}}{space 1}{space 1}{ralign 9:{res:{sf:    -1.05}}}{space 1}
{space 0}{space 0}{ralign 12:meanavera~t2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    4.456}}}{space 1}{space 1}{ralign 9:{res:{sf:      4.2}}}{space 1}{space 1}{ralign 9:{res:{sf:   -1.257}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    4.456}}}{space 1}{space 1}{ralign 9:{res:{sf:      4.2}}}{space 1}{space 1}{ralign 9:{res:{sf:   -1.244}}}{space 1}
{space 0}{space 0}{ralign 12:preclosing~w}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    507.9}}}{space 1}{space 1}{ralign 9:{res:{sf:  1620571}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.018}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    507.9}}}{space 1}{space 1}{ralign 9:{res:{sf:  1620668}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.498}}}{space 1}
{space 0}{space 0}{ralign 12:preage_of_~w}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    44.34}}}{space 1}{space 1}{ralign 9:{res:{sf:     1533}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.089}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    44.35}}}{space 1}{space 1}{ralign 9:{res:{sf:     1533}}}{space 1}{space 1}{ralign 9:{res:{sf:    1.835}}}{space 1}
{space 0}{space 0}{ralign 12:presumpaym~w}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .9038}}}{space 1}{space 1}{ralign 9:{res:{sf:    .3269}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.906}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .9038}}}{space 1}{space 1}{ralign 9:{res:{sf:    .3269}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.755}}}{space 1}
{res}{txt}
{com}. bysort customer: egen entropy_weight=mean(_webal)
{txt}(184655 missing values generated)

{com}. 
. *********************************
. **** Initial month (October) ****
. *********************************
. 
. probit paidcount ebalancematch [pweight=entropy_weight] if t==14 ,vce(cluster customer)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -4207.565}  
Iteration 1:{space 3}log pseudolikelihood = {res:-4183.6274}  
Iteration 2:{space 3}log pseudolikelihood = {res:-4183.6273}  
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}    10,096
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}     52.68
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-4183.6273{txt}{col 49}Pseudo R2{col 67}= {res}    0.0057

{txt}{ralign 79:(Std. Err. adjusted for {res:10,096} clusters in customer)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}    paidcount{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ebalancematch {c |}{col 15}{res}{space 2} .2229099{col 27}{space 2} .0307108{col 38}{space 1}    7.26{col 47}{space 3}0.000{col 55}{space 4} .1627179{col 68}{space 3} .2831019
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-.1498965{col 27}{space 2} .0206291{col 38}{space 1}   -7.27{col 47}{space 3}0.000{col 55}{space 4}-.1903287{col 68}{space 3}-.1094643
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(*) post
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}    10,096
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(paidcount), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:ebalancematch}{p_end}
{p2colreset}{...}

{res}{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27} Delta-method
{col 15}{c |}      dy/dx{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ebalancematch {c |}{col 15}{res}{space 2} .0883136{col 27}{space 2} .0120214{col 38}{space 1}    7.35{col 47}{space 3}0.000{col 55}{space 4}  .064752{col 68}{space 3} .1118751
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg lnpayment ebalancematch [pweight=entropy_weight] if t==14, vce(cluster customer)
{txt}(sum of wgt is 2,944.92359446082)

Linear regression                               Number of obs     = {res}     4,451
                                                {txt}F(1, 4450)        =  {res}     0.98
                                                {txt}Prob > F          = {res}    0.3223
                                                {txt}R-squared         = {res}    0.0003
                                                {txt}Root MSE          =    {res} 1.0928

{txt}{ralign 79:(Std. Err. adjusted for {res:4,451} clusters in customer)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}    lnpayment{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ebalancematch {c |}{col 15}{res}{space 2} .0378418{col 27}{space 2} .0382286{col 38}{space 1}    0.99{col 47}{space 3}0.322{col 55}{space 4}-.0371053{col 68}{space 3}  .112789
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 5.156991{col 27}{space 2}  .026678{col 38}{space 1}  193.31{col 47}{space 3}0.000{col 55}{space 4} 5.104688{col 68}{space 3} 5.209293
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. preserve 
{txt}
{com}. keep if t==14
{txt}(384,211 observations deleted)

{com}. capture program drop Ey_boot
{txt}
{com}. program define Ey_boot, eclass
{txt}  1{com}. twopm payment ebalancematch [pweight=entropy_weight], firstpart(probit) secondpart(regress, log) vce(cluster customer)
{txt}  2{com}. margins, dydx(ebalancematch) predict(duan) nose post
{txt}  3{com}. end
{txt}
{com}. bootstrap _b, seed(3125) reps(1000): Ey_boot
{txt}(running Ey_boot on estimation sample)

Bootstrap replications ({res}1000{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
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{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}    10,096
{txt}{col 49}Replications{col 67}= {res}     1,000

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}   Observed{col 27}   Bootstrap{col 55}         Norm{col 68}al-based
{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ebalancematch {c |}{col 15}{res}{space 2} 43.33195{col 27}{space 2} 8.728331{col 38}{space 1}    4.96{col 47}{space 3}0.000{col 55}{space 4} 26.22473{col 68}{space 3} 60.43916
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. restore
{txt}
{com}. 
. 
. reg asinhpayment ebalancematch [pweight=entropy_weight] if t==14, vce(cluster customer)
{txt}(sum of wgt is 6,074.27294973587)

Linear regression                               Number of obs     = {res}    10,096
                                                {txt}F(1, 10095)       =  {res}    54.06
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0079
                                                {txt}Root MSE          =    {res} 3.0193

{txt}{ralign 79:(Std. Err. adjusted for {res:10,096} clusters in customer)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1} asinhpayment{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ebalancematch {c |}{col 15}{res}{space 2} .5390649{col 27}{space 2} .0733143{col 38}{space 1}    7.35{col 47}{space 3}0.000{col 55}{space 4} .3953543{col 68}{space 3} .6827754
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}  2.57658{col 27}{space 2} .0482153{col 38}{space 1}   53.44{col 47}{space 3}0.000{col 55}{space 4} 2.482068{col 68}{space 3} 2.671092
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. *************************************
. **** Medium term (November-June) ****
. *************************************
. probit paidcount ebalancematch [pweight=entropy_weight] if t>=15 & t<=22 ,vce(cluster customer)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -31875.79}  
Iteration 1:{space 3}log pseudolikelihood = {res:-31806.889}  
Iteration 2:{space 3}log pseudolikelihood = {res:-31806.887}  
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}    80,026
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}     68.81
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-31806.887{txt}{col 49}Pseudo R2{col 67}= {res}    0.0022

{txt}{ralign 79:(Std. Err. adjusted for {res:10,075} clusters in customer)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}    paidcount{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ebalancematch {c |}{col 15}{res}{space 2} .1366224{col 27}{space 2} .0164701{col 38}{space 1}    8.30{col 47}{space 3}0.000{col 55}{space 4} .1043416{col 68}{space 3} .1689032
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-.3934848{col 27}{space 2} .0110139{col 38}{space 1}  -35.73{col 47}{space 3}0.000{col 55}{space 4}-.4150717{col 68}{space 3}-.3718979
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(*) post
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}    80,026
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(paidcount), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:ebalancematch}{p_end}
{p2colreset}{...}

{res}{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27} Delta-method
{col 15}{c |}      dy/dx{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ebalancematch {c |}{col 15}{res}{space 2} .0515914{col 27}{space 2} .0062097{col 38}{space 1}    8.31{col 47}{space 3}0.000{col 55}{space 4} .0394206{col 68}{space 3} .0637622
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg lnpayment ebalancematch  [pweight=entropy_weight] if t>=15 & t<=22 , vce(cluster customer)
{txt}(sum of wgt is 17,994.7915301197)

Linear regression                               Number of obs     = {res}    27,832
                                                {txt}F(1, 8532)        =  {res}     8.69
                                                {txt}Prob > F          = {res}    0.0032
                                                {txt}R-squared         = {res}    0.0012
                                                {txt}Root MSE          =    {res} 1.0347

{txt}{ralign 79:(Std. Err. adjusted for {res:8,533} clusters in customer)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}    lnpayment{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ebalancematch {c |}{col 15}{res}{space 2}-.0728982{col 27}{space 2} .0247317{col 38}{space 1}   -2.95{col 47}{space 3}0.003{col 55}{space 4}-.1213784{col 68}{space 3} -.024418
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 5.244444{col 27}{space 2} .0168703{col 38}{space 1}  310.87{col 47}{space 3}0.000{col 55}{space 4} 5.211374{col 68}{space 3} 5.277514
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. preserve 
{txt}
{com}. keep if t>=15 & t<=22 
{txt}(245,229 observations deleted)

{com}. capture program drop Ey_boot
{txt}
{com}. program define Ey_boot, eclass
{txt}  1{com}. twopm payment ebalancematch  [pweight=entropy_weight], firstpart(probit) secondpart(regress, log) vce(cluster customer)
{txt}  2{com}. margins, dydx(ebalancematch) predict(duan) nose post
{txt}  3{com}. end
{txt}
{com}. bootstrap _b, seed(3125) reps(1000): Ey_boot
{txt}(running Ey_boot on estimation sample)

Bootstrap replications ({res}1000{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
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{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}    80,026
{txt}{col 49}Replications{col 67}= {res}     1,000

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}   Observed{col 27}   Bootstrap{col 55}         Norm{col 68}al-based
{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ebalancematch {c |}{col 15}{res}{space 2}  8.92912{col 27}{space 2}  2.46649{col 38}{space 1}    3.62{col 47}{space 3}0.000{col 55}{space 4} 4.094888{col 68}{space 3} 13.76335
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. restore
{txt}
{com}. 
. reg asinhpayment ebalancematch [pweight=entropy_weight] if t>=15 & t<=22, vce(cluster customer)
{txt}(sum of wgt is 48,263.6036374811)

Linear regression                               Number of obs     = {res}    80,026
                                                {txt}F(1, 10074)       =  {res}    60.56
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0023
                                                {txt}Root MSE          =    {res} 2.9183

{txt}{ralign 79:(Std. Err. adjusted for {res:10,075} clusters in customer)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1} asinhpayment{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ebalancematch {c |}{col 15}{res}{space 2} .2777257{col 27}{space 2} .0356887{col 38}{space 1}    7.78{col 47}{space 3}0.000{col 55}{space 4} .2077687{col 68}{space 3} .3476827
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.060248{col 27}{space 2} .0232269{col 38}{space 1}   88.70{col 47}{space 3}0.000{col 55}{space 4} 2.014719{col 68}{space 3} 2.105778
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ** Control means ***
. tabstat paidcount lnpayment payment asinhpayment [aweight=entropy_weight] if t==14, by(ebalancematch) statistics(mean sd n) columns(statistics) nototal

{txt}Summary for variables: paidcount lnpayment payment asinhpayment
{col 6}by categories of: ebalancematch 

{ralign 13:ebalancematch} {...}
{c |}      mean        sd         N
{hline 14}{c +}{hline 30}
{ralign 13:0} {...}
{c |}{...}
 {res} .4404231  .4964731      7055
{space 13} {...}
{txt}{c |}{...}
 {res} 5.156991  1.086127      2842
{space 13} {...}
{txt}{c |}{...}
 {res} 150.1988  469.2562      7055
{space 13} {...}
{txt}{c |}{...}
 {res}  2.57658  2.992529      7055
{txt}{hline 14}{c +}{hline 30}
{ralign 13:1} {...}
{c |}{...}
 {res} .5291023  .4992344      3041
{space 13} {...}
{txt}{c |}{...}
 {res} 5.194832  1.098407      1609
{space 13} {...}
{txt}{c |}{...}
 {res} 182.5855  499.5403      3041
{space 13} {...}
{txt}{c |}{...}
 {res} 3.115645  3.045828      3041
{txt}{hline 14}{c BT}{hline 30}

{com}. tabstat paidcount lnpayment payment asinhpayment [aweight=entropy_weight] if t>=15 & t<=22, by(ebalancematch) statistics(mean sd n) columns(statistics) nototal

{txt}Summary for variables: paidcount lnpayment payment asinhpayment
{col 6}by categories of: ebalancematch 

{ralign 13:ebalancematch} {...}
{c |}      mean        sd         N
{hline 14}{c +}{hline 30}
{ralign 13:0} {...}
{c |}{...}
 {res} .3469807  .4760138     55864
{space 13} {...}
{txt}{c |}{...}
 {res} 5.244444  1.020714     18200
{space 13} {...}
{txt}{c |}{...}
 {res} 114.1537  363.4493     55864
{space 13} {...}
{txt}{c |}{...}
 {res} 2.060248   2.88963     55864
{txt}{hline 14}{c +}{hline 30}
{ralign 13:1} {...}
{c |}{...}
 {res} .3986425   .489629     24162
{space 13} {...}
{txt}{c |}{...}
 {res} 5.171546  1.046789      9632
{space 13} {...}
{txt}{c |}{...}
 {res} 123.8063  338.0565     24162
{space 13} {...}
{txt}{c |}{...}
 {res} 2.337974  2.946588     24162
{txt}{hline 14}{c BT}{hline 30}

{com}. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/sebastiantonke/Desktop/Replication Package/Replication files experiment/output_logs/table 5.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}20 Jul 2023, 09:18:17
{txt}{.-}
{smcl}
{txt}{sf}{ul off}